A Construction Cost Flow Risk Assessment Model

H Odeyinka, J Lowe, A Kaka

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Various 'short cut' approaches have been adopted in cash flow forecasting ranging from the statistical, mathematical, simulation and the use of artificial intelligence techniques. However, the majority of these approaches failed to consider the issues of risks and uncertainties inherent in construction. As such, a wide variation is observable between the predicted cash flow profile and the actual. This study attempts to model the variation between predicted and actual cost flow due to inherent risks in construction. Data were obtained through questionnaire survey and empirical data collection. Contractors on individual projects were requested to score on a Likert type scale, the extent of occurrence of each identified risk variable that resulted in the variation between the predicted and actual cost flow profiles. An analysis of the responses, using ranking of the mean response enabled the study to focus on the most significant risk variables. The impact of these risk variables on cost flow forecast was then investigated by collecting data on predicted and actual cost flow from completed construction projects in order to determine their variation. Neural network was employed using the back propagation algorithm to develop the cost flow risk assessment model. The developed model was tested on 20 new projects with satisfactory predictions of variations between the forecast and actual cost flow at 30, 50, 70 and 100% completion stages.
    LanguageEnglish
    Title of host publicationUnknown Host Publication
    EditorsDavid Greenwood
    Place of PublicationReading
    Pages3-12
    Number of pages10
    Publication statusPublished - Sep 2002
    EventThe 18th Annual ARCOM Conference - Northumbria University, Newcastle upon Tyne, UK
    Duration: 1 Sep 2002 → …

    Conference

    ConferenceThe 18th Annual ARCOM Conference
    Period1/09/02 → …

    Fingerprint

    Risk assessment
    Costs
    Backpropagation algorithms
    Contractors
    Artificial intelligence
    Neural networks

    Keywords

    • cost flow
    • contractors
    • modelling
    • neural network
    • risks and uncertainties

    Cite this

    Odeyinka, H., Lowe, J., & Kaka, A. (2002). A Construction Cost Flow Risk Assessment Model. In D. Greenwood (Ed.), Unknown Host Publication (pp. 3-12). Reading.
    Odeyinka, H ; Lowe, J ; Kaka, A. / A Construction Cost Flow Risk Assessment Model. Unknown Host Publication. editor / David Greenwood. Reading, 2002. pp. 3-12
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    title = "A Construction Cost Flow Risk Assessment Model",
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    author = "H Odeyinka and J Lowe and A Kaka",
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    Odeyinka, H, Lowe, J & Kaka, A 2002, A Construction Cost Flow Risk Assessment Model. in D Greenwood (ed.), Unknown Host Publication. Reading, pp. 3-12, The 18th Annual ARCOM Conference, 1/09/02.

    A Construction Cost Flow Risk Assessment Model. / Odeyinka, H; Lowe, J; Kaka, A.

    Unknown Host Publication. ed. / David Greenwood. Reading, 2002. p. 3-12.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    TY - GEN

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    AU - Lowe, J

    AU - Kaka, A

    PY - 2002/9

    Y1 - 2002/9

    N2 - Various 'short cut' approaches have been adopted in cash flow forecasting ranging from the statistical, mathematical, simulation and the use of artificial intelligence techniques. However, the majority of these approaches failed to consider the issues of risks and uncertainties inherent in construction. As such, a wide variation is observable between the predicted cash flow profile and the actual. This study attempts to model the variation between predicted and actual cost flow due to inherent risks in construction. Data were obtained through questionnaire survey and empirical data collection. Contractors on individual projects were requested to score on a Likert type scale, the extent of occurrence of each identified risk variable that resulted in the variation between the predicted and actual cost flow profiles. An analysis of the responses, using ranking of the mean response enabled the study to focus on the most significant risk variables. The impact of these risk variables on cost flow forecast was then investigated by collecting data on predicted and actual cost flow from completed construction projects in order to determine their variation. Neural network was employed using the back propagation algorithm to develop the cost flow risk assessment model. The developed model was tested on 20 new projects with satisfactory predictions of variations between the forecast and actual cost flow at 30, 50, 70 and 100% completion stages.

    AB - Various 'short cut' approaches have been adopted in cash flow forecasting ranging from the statistical, mathematical, simulation and the use of artificial intelligence techniques. However, the majority of these approaches failed to consider the issues of risks and uncertainties inherent in construction. As such, a wide variation is observable between the predicted cash flow profile and the actual. This study attempts to model the variation between predicted and actual cost flow due to inherent risks in construction. Data were obtained through questionnaire survey and empirical data collection. Contractors on individual projects were requested to score on a Likert type scale, the extent of occurrence of each identified risk variable that resulted in the variation between the predicted and actual cost flow profiles. An analysis of the responses, using ranking of the mean response enabled the study to focus on the most significant risk variables. The impact of these risk variables on cost flow forecast was then investigated by collecting data on predicted and actual cost flow from completed construction projects in order to determine their variation. Neural network was employed using the back propagation algorithm to develop the cost flow risk assessment model. The developed model was tested on 20 new projects with satisfactory predictions of variations between the forecast and actual cost flow at 30, 50, 70 and 100% completion stages.

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    KW - modelling

    KW - neural network

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    Odeyinka H, Lowe J, Kaka A. A Construction Cost Flow Risk Assessment Model. In Greenwood D, editor, Unknown Host Publication. Reading. 2002. p. 3-12